Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [87]:
data_dir = '/'
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [88]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[88]:
<matplotlib.image.AxesImage at 0x7f717a765c88>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [89]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[89]:
<matplotlib.image.AxesImage at 0x7f717a6caf60>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [90]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.2.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [91]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    
    images = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels))
    z = tf.placeholder(tf.float32, shape=(None, z_dim))
    lr = tf.placeholder(tf.float32, shape=())
    return images, z, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [92]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    alpha = 0.2
    keep_prob = 0.9
    
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        relu1 = tf.maximum(alpha * x1, x1)
        d1 = tf.nn.dropout(relu1, keep_prob)
        # 14x14x64
        
        x2 = tf.layers.conv2d(d1, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        d2 = tf.nn.dropout(relu2, keep_prob)
        # 7x7x128
        
        x3 = tf.layers.conv2d(d2, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        d3 = tf.nn.dropout(relu3, keep_prob)
        # 4x4x256
        
        # Flatten
        flat = tf.reshape(d3, (-1, 4 * 4 * 256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [93]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    alpha = 0.2
    
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 4 * 4 * 512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 4, 4, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 4x4x512 now
        
        # using valid padding and stride 1 to get size of 7 instead of 8
        x2 = tf.layers.conv2d_transpose(x1, 256, 4, strides=1, padding='valid', kernel_initializer=tf.contrib.layers.xavier_initializer())
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 7x7x256 now
        
        x2a = tf.layers.conv2d_transpose(x2, 256, 5, strides=1, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        x2a = tf.layers.batch_normalization(x2a, training=is_train)
        x2a = tf.maximum(alpha * x2a, x2a)
        # one more 7x7x256
        
        x3 = tf.layers.conv2d_transpose(x2a, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 14x14x128 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        # 28x28x3 now
        
        out = tf.tanh(logits)

        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [94]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function

    smooth = 0.1
    
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * (1 - smooth)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [95]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [96]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [97]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    out_channel_dim = data_shape[3]
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, out_channel_dim)
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_i, batch_images in enumerate(get_batches(batch_size)):
                # TODO: Train Model
                
                batch_images *= 2
                
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
                
                if batch_i % 10 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}, batch {}...".format(epoch_i + 1, epoch_count, batch_i),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                if batch_i % 100 == 0:
                    show_generator_output(sess, 16, input_z, out_channel_dim, data_image_mode)
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [98]:
batch_size = 32
z_dim = 200
learning_rate = 0.0004
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2, batch 0... Discriminator Loss: 9.1441... Generator Loss: 0.0003
Epoch 1/2, batch 10... Discriminator Loss: 3.1703... Generator Loss: 0.5977
Epoch 1/2, batch 20... Discriminator Loss: 3.0857... Generator Loss: 0.0748
Epoch 1/2, batch 30... Discriminator Loss: 1.8313... Generator Loss: 1.4853
Epoch 1/2, batch 40... Discriminator Loss: 1.6704... Generator Loss: 1.4134
Epoch 1/2, batch 50... Discriminator Loss: 2.1404... Generator Loss: 0.6451
Epoch 1/2, batch 60... Discriminator Loss: 2.0073... Generator Loss: 0.4250
Epoch 1/2, batch 70... Discriminator Loss: 2.6584... Generator Loss: 0.2325
Epoch 1/2, batch 80... Discriminator Loss: 1.8411... Generator Loss: 0.6385
Epoch 1/2, batch 90... Discriminator Loss: 1.8939... Generator Loss: 0.5770
Epoch 1/2, batch 100... Discriminator Loss: 1.6446... Generator Loss: 0.6098
Epoch 1/2, batch 110... Discriminator Loss: 2.1288... Generator Loss: 0.3716
Epoch 1/2, batch 120... Discriminator Loss: 1.6919... Generator Loss: 0.6354
Epoch 1/2, batch 130... Discriminator Loss: 1.7267... Generator Loss: 0.6925
Epoch 1/2, batch 140... Discriminator Loss: 1.7782... Generator Loss: 0.3767
Epoch 1/2, batch 150... Discriminator Loss: 1.7654... Generator Loss: 0.3780
Epoch 1/2, batch 160... Discriminator Loss: 1.7352... Generator Loss: 0.7338
Epoch 1/2, batch 170... Discriminator Loss: 1.5365... Generator Loss: 0.9689
Epoch 1/2, batch 180... Discriminator Loss: 1.5739... Generator Loss: 0.5317
Epoch 1/2, batch 190... Discriminator Loss: 1.6539... Generator Loss: 0.5133
Epoch 1/2, batch 200... Discriminator Loss: 1.5909... Generator Loss: 0.5636
Epoch 1/2, batch 210... Discriminator Loss: 1.6901... Generator Loss: 0.6580
Epoch 1/2, batch 220... Discriminator Loss: 1.4519... Generator Loss: 0.7817
Epoch 1/2, batch 230... Discriminator Loss: 1.7155... Generator Loss: 0.7771
Epoch 1/2, batch 240... Discriminator Loss: 1.7625... Generator Loss: 0.4229
Epoch 1/2, batch 250... Discriminator Loss: 1.8093... Generator Loss: 0.3662
Epoch 1/2, batch 260... Discriminator Loss: 1.5733... Generator Loss: 0.6780
Epoch 1/2, batch 270... Discriminator Loss: 1.5006... Generator Loss: 0.5916
Epoch 1/2, batch 280... Discriminator Loss: 1.6265... Generator Loss: 0.5171
Epoch 1/2, batch 290... Discriminator Loss: 1.6861... Generator Loss: 0.3412
Epoch 1/2, batch 300... Discriminator Loss: 1.5885... Generator Loss: 0.8684
Epoch 1/2, batch 310... Discriminator Loss: 1.5433... Generator Loss: 0.7663
Epoch 1/2, batch 320... Discriminator Loss: 1.7591... Generator Loss: 0.3484
Epoch 1/2, batch 330... Discriminator Loss: 1.6951... Generator Loss: 0.4582
Epoch 1/2, batch 340... Discriminator Loss: 1.5330... Generator Loss: 0.5843
Epoch 1/2, batch 350... Discriminator Loss: 1.5412... Generator Loss: 0.5413
Epoch 1/2, batch 360... Discriminator Loss: 1.5728... Generator Loss: 0.5612
Epoch 1/2, batch 370... Discriminator Loss: 1.6054... Generator Loss: 0.5627
Epoch 1/2, batch 380... Discriminator Loss: 1.4866... Generator Loss: 0.7401
Epoch 1/2, batch 390... Discriminator Loss: 1.5733... Generator Loss: 0.5349
Epoch 1/2, batch 400... Discriminator Loss: 1.5604... Generator Loss: 0.6321
Epoch 1/2, batch 410... Discriminator Loss: 1.6385... Generator Loss: 0.4403
Epoch 1/2, batch 420... Discriminator Loss: 1.5897... Generator Loss: 0.4885
Epoch 1/2, batch 430... Discriminator Loss: 1.6272... Generator Loss: 0.6428
Epoch 1/2, batch 440... Discriminator Loss: 1.5516... Generator Loss: 0.4633
Epoch 1/2, batch 450... Discriminator Loss: 1.4124... Generator Loss: 0.6571
Epoch 1/2, batch 460... Discriminator Loss: 1.5660... Generator Loss: 0.6832
Epoch 1/2, batch 470... Discriminator Loss: 1.6357... Generator Loss: 0.9428
Epoch 1/2, batch 480... Discriminator Loss: 1.5823... Generator Loss: 0.7742
Epoch 1/2, batch 490... Discriminator Loss: 1.4599... Generator Loss: 0.8161
Epoch 1/2, batch 500... Discriminator Loss: 1.5212... Generator Loss: 1.0023
Epoch 1/2, batch 510... Discriminator Loss: 1.6573... Generator Loss: 0.4299
Epoch 1/2, batch 520... Discriminator Loss: 1.5236... Generator Loss: 0.7033
Epoch 1/2, batch 530... Discriminator Loss: 1.6203... Generator Loss: 0.5018
Epoch 1/2, batch 540... Discriminator Loss: 1.5708... Generator Loss: 0.8991
Epoch 1/2, batch 550... Discriminator Loss: 1.5295... Generator Loss: 0.7572
Epoch 1/2, batch 560... Discriminator Loss: 1.5387... Generator Loss: 0.7276
Epoch 1/2, batch 570... Discriminator Loss: 1.4870... Generator Loss: 0.7436
Epoch 1/2, batch 580... Discriminator Loss: 1.4734... Generator Loss: 0.8182
Epoch 1/2, batch 590... Discriminator Loss: 1.5897... Generator Loss: 0.6082
Epoch 1/2, batch 600... Discriminator Loss: 1.4688... Generator Loss: 0.7062
Epoch 1/2, batch 610... Discriminator Loss: 1.4965... Generator Loss: 0.6652
Epoch 1/2, batch 620... Discriminator Loss: 1.4591... Generator Loss: 0.7810
Epoch 1/2, batch 630... Discriminator Loss: 1.5136... Generator Loss: 0.7396
Epoch 1/2, batch 640... Discriminator Loss: 1.4157... Generator Loss: 0.9141
Epoch 1/2, batch 650... Discriminator Loss: 1.5489... Generator Loss: 0.9344
Epoch 1/2, batch 660... Discriminator Loss: 1.5580... Generator Loss: 0.5343
Epoch 1/2, batch 670... Discriminator Loss: 1.4579... Generator Loss: 0.9235
Epoch 1/2, batch 680... Discriminator Loss: 1.4483... Generator Loss: 0.7727
Epoch 1/2, batch 690... Discriminator Loss: 1.5358... Generator Loss: 0.7772
Epoch 1/2, batch 700... Discriminator Loss: 1.4836... Generator Loss: 0.9586
Epoch 1/2, batch 710... Discriminator Loss: 1.4773... Generator Loss: 0.7412
Epoch 1/2, batch 720... Discriminator Loss: 1.5279... Generator Loss: 0.7500
Epoch 1/2, batch 730... Discriminator Loss: 1.5667... Generator Loss: 0.7443
Epoch 1/2, batch 740... Discriminator Loss: 1.4521... Generator Loss: 0.6380
Epoch 1/2, batch 750... Discriminator Loss: 1.3936... Generator Loss: 0.5435
Epoch 1/2, batch 760... Discriminator Loss: 1.4530... Generator Loss: 0.6391
Epoch 1/2, batch 770... Discriminator Loss: 1.4362... Generator Loss: 0.6084
Epoch 1/2, batch 780... Discriminator Loss: 1.5680... Generator Loss: 0.7237
Epoch 1/2, batch 790... Discriminator Loss: 1.4965... Generator Loss: 0.5236
Epoch 1/2, batch 800... Discriminator Loss: 1.4181... Generator Loss: 0.6295
Epoch 1/2, batch 810... Discriminator Loss: 1.4982... Generator Loss: 0.7041
Epoch 1/2, batch 820... Discriminator Loss: 1.4653... Generator Loss: 0.7664
Epoch 1/2, batch 830... Discriminator Loss: 1.5265... Generator Loss: 0.4082
Epoch 1/2, batch 840... Discriminator Loss: 1.3657... Generator Loss: 0.8507
Epoch 1/2, batch 850... Discriminator Loss: 1.4253... Generator Loss: 0.5536
Epoch 1/2, batch 860... Discriminator Loss: 1.5090... Generator Loss: 0.5136
Epoch 1/2, batch 870... Discriminator Loss: 1.4481... Generator Loss: 0.6037
Epoch 1/2, batch 880... Discriminator Loss: 1.5982... Generator Loss: 0.5052
Epoch 1/2, batch 890... Discriminator Loss: 1.3971... Generator Loss: 0.7081
Epoch 1/2, batch 900... Discriminator Loss: 1.4224... Generator Loss: 0.6796
Epoch 1/2, batch 910... Discriminator Loss: 1.5792... Generator Loss: 0.8969
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Epoch 2/2, batch 1760... Discriminator Loss: 1.2120... Generator Loss: 0.9764
Epoch 2/2, batch 1770... Discriminator Loss: 1.2810... Generator Loss: 0.7660
Epoch 2/2, batch 1780... Discriminator Loss: 1.8105... Generator Loss: 0.4067
Epoch 2/2, batch 1790... Discriminator Loss: 1.3415... Generator Loss: 0.4816
Epoch 2/2, batch 1800... Discriminator Loss: 1.3612... Generator Loss: 0.6305
Epoch 2/2, batch 1810... Discriminator Loss: 1.2910... Generator Loss: 0.3960
Epoch 2/2, batch 1820... Discriminator Loss: 1.1133... Generator Loss: 1.0025
Epoch 2/2, batch 1830... Discriminator Loss: 1.2968... Generator Loss: 0.5699
Epoch 2/2, batch 1840... Discriminator Loss: 1.3484... Generator Loss: 1.1779
Epoch 2/2, batch 1850... Discriminator Loss: 1.3184... Generator Loss: 0.6560
Epoch 2/2, batch 1860... Discriminator Loss: 1.3008... Generator Loss: 0.9792
Epoch 2/2, batch 1870... Discriminator Loss: 1.4470... Generator Loss: 1.5309

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [99]:
batch_size = 32
z_dim = 200
learning_rate = 0.0004
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1, batch 0... Discriminator Loss: 7.5461... Generator Loss: 0.0011
Epoch 1/1, batch 10... Discriminator Loss: 4.7342... Generator Loss: 0.1262
Epoch 1/1, batch 20... Discriminator Loss: 1.9305... Generator Loss: 2.4878
Epoch 1/1, batch 30... Discriminator Loss: 3.1071... Generator Loss: 0.2485
Epoch 1/1, batch 40... Discriminator Loss: 2.3938... Generator Loss: 0.2577
Epoch 1/1, batch 50... Discriminator Loss: 1.5882... Generator Loss: 0.8764
Epoch 1/1, batch 60... Discriminator Loss: 1.0469... Generator Loss: 1.3668
Epoch 1/1, batch 70... Discriminator Loss: 1.4818... Generator Loss: 0.6690
Epoch 1/1, batch 80... Discriminator Loss: 1.7271... Generator Loss: 0.4186
Epoch 1/1, batch 90... Discriminator Loss: 2.4079... Generator Loss: 0.3199
Epoch 1/1, batch 100... Discriminator Loss: 1.7644... Generator Loss: 0.7007
Epoch 1/1, batch 110... Discriminator Loss: 1.8221... Generator Loss: 0.6272
Epoch 1/1, batch 120... Discriminator Loss: 1.5443... Generator Loss: 0.7713
Epoch 1/1, batch 130... Discriminator Loss: 1.5523... Generator Loss: 0.8534
Epoch 1/1, batch 140... Discriminator Loss: 1.7848... Generator Loss: 0.6403
Epoch 1/1, batch 150... Discriminator Loss: 1.5166... Generator Loss: 0.5791
Epoch 1/1, batch 160... Discriminator Loss: 1.9387... Generator Loss: 0.5088
Epoch 1/1, batch 170... Discriminator Loss: 1.3055... Generator Loss: 1.0492
Epoch 1/1, batch 180... Discriminator Loss: 1.4986... Generator Loss: 0.5659
Epoch 1/1, batch 190... Discriminator Loss: 1.4770... Generator Loss: 0.8433
Epoch 1/1, batch 200... Discriminator Loss: 2.0370... Generator Loss: 0.5153
Epoch 1/1, batch 210... Discriminator Loss: 1.6607... Generator Loss: 0.5829
Epoch 1/1, batch 220... Discriminator Loss: 1.4105... Generator Loss: 0.7938
Epoch 1/1, batch 230... Discriminator Loss: 1.3282... Generator Loss: 0.9050
Epoch 1/1, batch 240... Discriminator Loss: 1.4548... Generator Loss: 0.7110
Epoch 1/1, batch 250... Discriminator Loss: 1.3452... Generator Loss: 0.8339
Epoch 1/1, batch 260... Discriminator Loss: 1.5494... Generator Loss: 0.7284
Epoch 1/1, batch 270... Discriminator Loss: 1.6153... Generator Loss: 0.7640
Epoch 1/1, batch 280... Discriminator Loss: 1.4349... Generator Loss: 0.6981
Epoch 1/1, batch 290... Discriminator Loss: 1.7631... Generator Loss: 0.4976
Epoch 1/1, batch 300... Discriminator Loss: 1.5339... Generator Loss: 0.7087
Epoch 1/1, batch 310... Discriminator Loss: 1.5951... Generator Loss: 0.6109
Epoch 1/1, batch 320... Discriminator Loss: 1.5311... Generator Loss: 0.7123
Epoch 1/1, batch 330... Discriminator Loss: 1.3801... Generator Loss: 0.8168
Epoch 1/1, batch 340... Discriminator Loss: 1.6412... Generator Loss: 0.7335
Epoch 1/1, batch 350... Discriminator Loss: 1.4615... Generator Loss: 0.7816
Epoch 1/1, batch 360... Discriminator Loss: 1.3224... Generator Loss: 0.9772
Epoch 1/1, batch 370... Discriminator Loss: 1.4999... Generator Loss: 0.8221
Epoch 1/1, batch 380... Discriminator Loss: 1.5475... Generator Loss: 0.6451
Epoch 1/1, batch 390... Discriminator Loss: 1.4298... Generator Loss: 0.7692
Epoch 1/1, batch 400... Discriminator Loss: 1.3472... Generator Loss: 0.7735
Epoch 1/1, batch 410... Discriminator Loss: 1.6325... Generator Loss: 0.5945
Epoch 1/1, batch 420... Discriminator Loss: 1.7375... Generator Loss: 0.6974
Epoch 1/1, batch 430... Discriminator Loss: 1.4720... Generator Loss: 0.7998
Epoch 1/1, batch 440... Discriminator Loss: 1.6472... Generator Loss: 0.6897
Epoch 1/1, batch 450... Discriminator Loss: 1.5445... Generator Loss: 0.6798
Epoch 1/1, batch 460... Discriminator Loss: 1.4365... Generator Loss: 0.8335
Epoch 1/1, batch 470... Discriminator Loss: 1.6871... Generator Loss: 0.6240
Epoch 1/1, batch 480... Discriminator Loss: 1.6748... Generator Loss: 0.6679
Epoch 1/1, batch 490... Discriminator Loss: 1.4798... Generator Loss: 0.7673
Epoch 1/1, batch 500... Discriminator Loss: 1.4484... Generator Loss: 0.8290
Epoch 1/1, batch 510... Discriminator Loss: 1.5660... Generator Loss: 0.6318
Epoch 1/1, batch 520... Discriminator Loss: 1.5613... Generator Loss: 0.5458
Epoch 1/1, batch 530... Discriminator Loss: 1.6495... Generator Loss: 0.7046
Epoch 1/1, batch 540... Discriminator Loss: 1.5441... Generator Loss: 0.7379
Epoch 1/1, batch 550... Discriminator Loss: 1.6025... Generator Loss: 0.6971
Epoch 1/1, batch 560... Discriminator Loss: 1.5643... Generator Loss: 0.7108
Epoch 1/1, batch 570... Discriminator Loss: 1.5726... Generator Loss: 0.7529
Epoch 1/1, batch 580... Discriminator Loss: 1.5481... Generator Loss: 0.7135
Epoch 1/1, batch 590... Discriminator Loss: 1.5043... Generator Loss: 0.6505
Epoch 1/1, batch 600... Discriminator Loss: 1.5847... Generator Loss: 0.6887
Epoch 1/1, batch 610... Discriminator Loss: 1.4655... Generator Loss: 0.7277
Epoch 1/1, batch 620... Discriminator Loss: 1.4159... Generator Loss: 0.8114
Epoch 1/1, batch 630... Discriminator Loss: 1.4825... Generator Loss: 0.5878
Epoch 1/1, batch 640... Discriminator Loss: 1.5906... Generator Loss: 0.6577
Epoch 1/1, batch 650... Discriminator Loss: 1.4513... Generator Loss: 0.7585
Epoch 1/1, batch 660... Discriminator Loss: 1.5661... Generator Loss: 0.6274
Epoch 1/1, batch 670... Discriminator Loss: 1.4690... Generator Loss: 0.7758
Epoch 1/1, batch 680... Discriminator Loss: 1.4755... Generator Loss: 0.9447
Epoch 1/1, batch 690... Discriminator Loss: 1.4897... Generator Loss: 0.7424
Epoch 1/1, batch 700... Discriminator Loss: 1.4355... Generator Loss: 0.7466
Epoch 1/1, batch 710... Discriminator Loss: 1.4600... Generator Loss: 0.7705
Epoch 1/1, batch 720... Discriminator Loss: 1.4812... Generator Loss: 0.7093
Epoch 1/1, batch 730... Discriminator Loss: 1.5391... Generator Loss: 0.6714
Epoch 1/1, batch 740... Discriminator Loss: 1.3694... Generator Loss: 0.7862
Epoch 1/1, batch 750... Discriminator Loss: 1.4896... Generator Loss: 0.7259
Epoch 1/1, batch 760... Discriminator Loss: 1.5154... Generator Loss: 0.7610
Epoch 1/1, batch 770... Discriminator Loss: 1.4962... Generator Loss: 0.6512
Epoch 1/1, batch 780... Discriminator Loss: 1.4718... Generator Loss: 0.6978
Epoch 1/1, batch 790... Discriminator Loss: 1.4720... Generator Loss: 0.6091
Epoch 1/1, batch 800... Discriminator Loss: 1.6353... Generator Loss: 0.5868
Epoch 1/1, batch 810... Discriminator Loss: 1.4953... Generator Loss: 0.7196
Epoch 1/1, batch 820... Discriminator Loss: 1.5267... Generator Loss: 0.7031
Epoch 1/1, batch 830... Discriminator Loss: 1.4535... Generator Loss: 0.7725
Epoch 1/1, batch 840... Discriminator Loss: 1.4993... Generator Loss: 0.6989
Epoch 1/1, batch 850... Discriminator Loss: 1.5657... Generator Loss: 0.6563
Epoch 1/1, batch 860... Discriminator Loss: 1.4847... Generator Loss: 0.7711
Epoch 1/1, batch 870... Discriminator Loss: 1.4343... Generator Loss: 0.7360
Epoch 1/1, batch 880... Discriminator Loss: 1.4175... Generator Loss: 0.7726
Epoch 1/1, batch 890... Discriminator Loss: 1.4966... Generator Loss: 0.7668
Epoch 1/1, batch 900... Discriminator Loss: 1.4917... Generator Loss: 0.7276
Epoch 1/1, batch 910... Discriminator Loss: 1.5042... Generator Loss: 0.6897
Epoch 1/1, batch 920... Discriminator Loss: 1.4922... Generator Loss: 0.6997
Epoch 1/1, batch 930... Discriminator Loss: 1.4616... Generator Loss: 0.7252
Epoch 1/1, batch 940... Discriminator Loss: 1.4983... Generator Loss: 0.7238
Epoch 1/1, batch 950... Discriminator Loss: 1.4384... Generator Loss: 0.7143
Epoch 1/1, batch 960... Discriminator Loss: 1.4426... Generator Loss: 0.6411
Epoch 1/1, batch 970... Discriminator Loss: 1.4589... Generator Loss: 0.7415
Epoch 1/1, batch 980... Discriminator Loss: 1.5606... Generator Loss: 0.7523
Epoch 1/1, batch 990... Discriminator Loss: 1.4697... Generator Loss: 0.8488
Epoch 1/1, batch 1000... Discriminator Loss: 1.4765... Generator Loss: 0.7097
Epoch 1/1, batch 1010... Discriminator Loss: 1.4950... Generator Loss: 0.7642
Epoch 1/1, batch 1020... Discriminator Loss: 1.4069... Generator Loss: 0.7732
Epoch 1/1, batch 1030... Discriminator Loss: 1.3722... Generator Loss: 0.7601
Epoch 1/1, batch 1040... Discriminator Loss: 1.4235... Generator Loss: 0.6668
Epoch 1/1, batch 1050... Discriminator Loss: 1.4998... Generator Loss: 0.6723
Epoch 1/1, batch 1060... Discriminator Loss: 1.4621... Generator Loss: 0.8366
Epoch 1/1, batch 1070... Discriminator Loss: 1.4057... Generator Loss: 0.7893
Epoch 1/1, batch 1080... Discriminator Loss: 1.3664... Generator Loss: 0.7778
Epoch 1/1, batch 1090... Discriminator Loss: 1.4797... Generator Loss: 0.7628
Epoch 1/1, batch 1100... Discriminator Loss: 1.4531... Generator Loss: 0.7340
Epoch 1/1, batch 1110... Discriminator Loss: 1.5290... Generator Loss: 0.6915
Epoch 1/1, batch 1120... Discriminator Loss: 1.3746... Generator Loss: 0.8375
Epoch 1/1, batch 1130... Discriminator Loss: 1.4300... Generator Loss: 0.7910
Epoch 1/1, batch 1140... Discriminator Loss: 1.3912... Generator Loss: 0.8697
Epoch 1/1, batch 1150... Discriminator Loss: 1.4697... Generator Loss: 0.6260
Epoch 1/1, batch 1160... Discriminator Loss: 1.4138... Generator Loss: 0.7826
Epoch 1/1, batch 1170... Discriminator Loss: 1.4416... Generator Loss: 0.7666
Epoch 1/1, batch 1180... Discriminator Loss: 1.4939... Generator Loss: 0.7695
Epoch 1/1, batch 1190... Discriminator Loss: 1.4383... Generator Loss: 0.7955
Epoch 1/1, batch 1200... Discriminator Loss: 1.5560... Generator Loss: 0.7137
Epoch 1/1, batch 1210... Discriminator Loss: 1.4480... Generator Loss: 0.8931
Epoch 1/1, batch 1220... Discriminator Loss: 1.4656... Generator Loss: 0.7605
Epoch 1/1, batch 1230... Discriminator Loss: 1.4893... Generator Loss: 0.7474
Epoch 1/1, batch 1240... Discriminator Loss: 1.4035... Generator Loss: 0.7935
Epoch 1/1, batch 1250... Discriminator Loss: 1.4179... Generator Loss: 0.8463
Epoch 1/1, batch 1260... Discriminator Loss: 1.4456... Generator Loss: 0.7180
Epoch 1/1, batch 1270... Discriminator Loss: 1.3842... Generator Loss: 0.7334
Epoch 1/1, batch 1280... Discriminator Loss: 1.4613... Generator Loss: 0.8440
Epoch 1/1, batch 1290... Discriminator Loss: 1.4183... Generator Loss: 0.7329
Epoch 1/1, batch 1300... Discriminator Loss: 1.4659... Generator Loss: 0.7697
Epoch 1/1, batch 1310... Discriminator Loss: 1.5204... Generator Loss: 0.7109
Epoch 1/1, batch 1320... Discriminator Loss: 1.3685... Generator Loss: 0.7419
Epoch 1/1, batch 1330... Discriminator Loss: 1.4130... Generator Loss: 0.7718
Epoch 1/1, batch 1340... Discriminator Loss: 1.4612... Generator Loss: 0.7541
Epoch 1/1, batch 1350... Discriminator Loss: 1.4374... Generator Loss: 0.7564
Epoch 1/1, batch 1360... Discriminator Loss: 1.4420... Generator Loss: 0.7132
Epoch 1/1, batch 1370... Discriminator Loss: 1.4572... Generator Loss: 0.7702
Epoch 1/1, batch 1380... Discriminator Loss: 1.4055... Generator Loss: 0.7177
Epoch 1/1, batch 1390... Discriminator Loss: 1.3757... Generator Loss: 0.7952
Epoch 1/1, batch 1400... Discriminator Loss: 1.4080... Generator Loss: 0.6971
Epoch 1/1, batch 1410... Discriminator Loss: 1.4649... Generator Loss: 0.6823
Epoch 1/1, batch 1420... Discriminator Loss: 1.4909... Generator Loss: 0.7128
Epoch 1/1, batch 1430... Discriminator Loss: 1.4016... Generator Loss: 0.7911
Epoch 1/1, batch 1440... Discriminator Loss: 1.4809... Generator Loss: 0.6894
Epoch 1/1, batch 1450... Discriminator Loss: 1.4287... Generator Loss: 0.7241
Epoch 1/1, batch 1460... Discriminator Loss: 1.4625... Generator Loss: 0.6779
Epoch 1/1, batch 1470... Discriminator Loss: 1.4656... Generator Loss: 0.7559
Epoch 1/1, batch 1480... Discriminator Loss: 1.4792... Generator Loss: 0.6112
Epoch 1/1, batch 1490... Discriminator Loss: 1.4376... Generator Loss: 0.6978
Epoch 1/1, batch 1500... Discriminator Loss: 1.5165... Generator Loss: 0.6598
Epoch 1/1, batch 1510... Discriminator Loss: 1.4535... Generator Loss: 0.7119
Epoch 1/1, batch 1520... Discriminator Loss: 1.4163... Generator Loss: 0.7740
Epoch 1/1, batch 1530... Discriminator Loss: 1.4814... Generator Loss: 0.6970
Epoch 1/1, batch 1540... Discriminator Loss: 1.3985... Generator Loss: 0.7149
Epoch 1/1, batch 1550... Discriminator Loss: 1.4267... Generator Loss: 0.7470
Epoch 1/1, batch 1560... Discriminator Loss: 1.4133... Generator Loss: 0.7662
Epoch 1/1, batch 1570... Discriminator Loss: 1.4261... Generator Loss: 0.7350
Epoch 1/1, batch 1580... Discriminator Loss: 1.4600... Generator Loss: 0.6568
Epoch 1/1, batch 1590... Discriminator Loss: 1.4399... Generator Loss: 0.7343
Epoch 1/1, batch 1600... Discriminator Loss: 1.4638... Generator Loss: 0.7962
Epoch 1/1, batch 1610... Discriminator Loss: 1.3983... Generator Loss: 0.8551
Epoch 1/1, batch 1620... Discriminator Loss: 1.4330... Generator Loss: 0.6688
Epoch 1/1, batch 1630... Discriminator Loss: 1.3978... Generator Loss: 0.6834
Epoch 1/1, batch 1640... Discriminator Loss: 1.4089... Generator Loss: 0.7387
Epoch 1/1, batch 1650... Discriminator Loss: 1.4267... Generator Loss: 0.7966
Epoch 1/1, batch 1660... Discriminator Loss: 1.4524... Generator Loss: 0.7600
Epoch 1/1, batch 1670... Discriminator Loss: 1.4409... Generator Loss: 0.6842
Epoch 1/1, batch 1680... Discriminator Loss: 1.4227... Generator Loss: 0.7878
Epoch 1/1, batch 1690... Discriminator Loss: 1.3839... Generator Loss: 0.7622
Epoch 1/1, batch 1700... Discriminator Loss: 1.4378... Generator Loss: 0.7537
Epoch 1/1, batch 1710... Discriminator Loss: 1.4080... Generator Loss: 0.7124
Epoch 1/1, batch 1720... Discriminator Loss: 1.4832... Generator Loss: 0.6495
Epoch 1/1, batch 1730... Discriminator Loss: 1.4394... Generator Loss: 0.8601
Epoch 1/1, batch 1740... Discriminator Loss: 1.4936... Generator Loss: 0.6953
Epoch 1/1, batch 1750... Discriminator Loss: 1.4379... Generator Loss: 0.7039
Epoch 1/1, batch 1760... Discriminator Loss: 1.3801... Generator Loss: 0.6653
Epoch 1/1, batch 1770... Discriminator Loss: 1.4679... Generator Loss: 0.7235
Epoch 1/1, batch 1780... Discriminator Loss: 1.3864... Generator Loss: 0.8342
Epoch 1/1, batch 1790... Discriminator Loss: 1.4403... Generator Loss: 0.8704
Epoch 1/1, batch 1800... Discriminator Loss: 1.5882... Generator Loss: 0.6152
Epoch 1/1, batch 1810... Discriminator Loss: 1.4531... Generator Loss: 0.7187
Epoch 1/1, batch 1820... Discriminator Loss: 1.4431... Generator Loss: 0.7443
Epoch 1/1, batch 1830... Discriminator Loss: 1.3390... Generator Loss: 0.7931
Epoch 1/1, batch 1840... Discriminator Loss: 1.4407... Generator Loss: 0.7380
Epoch 1/1, batch 1850... Discriminator Loss: 1.4644... Generator Loss: 0.7474
Epoch 1/1, batch 1860... Discriminator Loss: 1.3836... Generator Loss: 0.7801
Epoch 1/1, batch 1870... Discriminator Loss: 1.4433... Generator Loss: 0.7252
Epoch 1/1, batch 1880... Discriminator Loss: 1.4587... Generator Loss: 0.7890
Epoch 1/1, batch 1890... Discriminator Loss: 1.4091... Generator Loss: 0.7931
Epoch 1/1, batch 1900... Discriminator Loss: 1.4495... Generator Loss: 0.6857
Epoch 1/1, batch 1910... Discriminator Loss: 1.4002... Generator Loss: 0.8441
Epoch 1/1, batch 1920... Discriminator Loss: 1.3861... Generator Loss: 0.7974
Epoch 1/1, batch 1930... Discriminator Loss: 1.4032... Generator Loss: 0.7832
Epoch 1/1, batch 1940... Discriminator Loss: 1.4903... Generator Loss: 0.6408
Epoch 1/1, batch 1950... Discriminator Loss: 1.4677... Generator Loss: 0.7343
Epoch 1/1, batch 1960... Discriminator Loss: 1.4258... Generator Loss: 0.7569
Epoch 1/1, batch 1970... Discriminator Loss: 1.4466... Generator Loss: 0.7619
Epoch 1/1, batch 1980... Discriminator Loss: 1.4306... Generator Loss: 0.7528
Epoch 1/1, batch 1990... Discriminator Loss: 1.4105... Generator Loss: 0.7696
Epoch 1/1, batch 2000... Discriminator Loss: 1.4653... Generator Loss: 0.7168
Epoch 1/1, batch 2010... Discriminator Loss: 1.4145... Generator Loss: 0.7797
Epoch 1/1, batch 2020... Discriminator Loss: 1.4272... Generator Loss: 0.6930
Epoch 1/1, batch 2030... Discriminator Loss: 1.4879... Generator Loss: 0.7736
Epoch 1/1, batch 2040... Discriminator Loss: 1.4963... Generator Loss: 0.7181
Epoch 1/1, batch 2050... Discriminator Loss: 1.3882... Generator Loss: 0.7435
Epoch 1/1, batch 2060... Discriminator Loss: 1.4283... Generator Loss: 0.7097
Epoch 1/1, batch 2070... Discriminator Loss: 1.4712... Generator Loss: 0.7632
Epoch 1/1, batch 2080... Discriminator Loss: 1.4025... Generator Loss: 0.7847
Epoch 1/1, batch 2090... Discriminator Loss: 1.4518... Generator Loss: 0.7201
Epoch 1/1, batch 2100... Discriminator Loss: 1.4175... Generator Loss: 0.7170
Epoch 1/1, batch 2110... Discriminator Loss: 1.4270... Generator Loss: 0.7573
Epoch 1/1, batch 2120... Discriminator Loss: 1.4390... Generator Loss: 0.7380
Epoch 1/1, batch 2130... Discriminator Loss: 1.4490... Generator Loss: 0.6754
Epoch 1/1, batch 2140... Discriminator Loss: 1.4308... Generator Loss: 0.7404
Epoch 1/1, batch 2150... Discriminator Loss: 1.4420... Generator Loss: 0.7634
Epoch 1/1, batch 2160... Discriminator Loss: 1.4509... Generator Loss: 0.7911
Epoch 1/1, batch 2170... Discriminator Loss: 1.4226... Generator Loss: 0.8801
Epoch 1/1, batch 2180... Discriminator Loss: 1.4397... Generator Loss: 0.7722
Epoch 1/1, batch 2190... Discriminator Loss: 1.4147... Generator Loss: 0.7680
Epoch 1/1, batch 2200... Discriminator Loss: 1.4216... Generator Loss: 0.6593
Epoch 1/1, batch 2210... Discriminator Loss: 1.3776... Generator Loss: 0.7611
Epoch 1/1, batch 2220... Discriminator Loss: 1.3763... Generator Loss: 0.8137
Epoch 1/1, batch 2230... Discriminator Loss: 1.4576... Generator Loss: 0.6788
Epoch 1/1, batch 2240... Discriminator Loss: 1.4066... Generator Loss: 0.7494
Epoch 1/1, batch 2250... Discriminator Loss: 1.4079... Generator Loss: 0.8628
Epoch 1/1, batch 2260... Discriminator Loss: 1.4054... Generator Loss: 0.8059
Epoch 1/1, batch 2270... Discriminator Loss: 1.4391... Generator Loss: 0.7817
Epoch 1/1, batch 2280... Discriminator Loss: 1.4027... Generator Loss: 0.8020
Epoch 1/1, batch 2290... Discriminator Loss: 1.4969... Generator Loss: 0.7261
Epoch 1/1, batch 2300... Discriminator Loss: 1.3960... Generator Loss: 0.7897
Epoch 1/1, batch 2310... Discriminator Loss: 1.4794... Generator Loss: 0.7937
Epoch 1/1, batch 2320... Discriminator Loss: 1.4276... Generator Loss: 0.7325
Epoch 1/1, batch 2330... Discriminator Loss: 1.4151... Generator Loss: 0.8105
Epoch 1/1, batch 2340... Discriminator Loss: 1.3847... Generator Loss: 0.7580
Epoch 1/1, batch 2350... Discriminator Loss: 1.4617... Generator Loss: 0.7340
Epoch 1/1, batch 2360... Discriminator Loss: 1.4279... Generator Loss: 0.8171
Epoch 1/1, batch 2370... Discriminator Loss: 1.4390... Generator Loss: 0.7370
Epoch 1/1, batch 2380... Discriminator Loss: 1.4428... Generator Loss: 0.7369
Epoch 1/1, batch 2390... Discriminator Loss: 1.4044... Generator Loss: 0.7576
Epoch 1/1, batch 2400... Discriminator Loss: 1.4643... Generator Loss: 0.7335
Epoch 1/1, batch 2410... Discriminator Loss: 1.3972... Generator Loss: 0.8389
Epoch 1/1, batch 2420... Discriminator Loss: 1.4191... Generator Loss: 0.7698
Epoch 1/1, batch 2430... Discriminator Loss: 1.4396... Generator Loss: 0.9073
Epoch 1/1, batch 2440... Discriminator Loss: 1.5279... Generator Loss: 0.6924
Epoch 1/1, batch 2450... Discriminator Loss: 1.4256... Generator Loss: 0.8213
Epoch 1/1, batch 2460... Discriminator Loss: 1.4253... Generator Loss: 0.7442
Epoch 1/1, batch 2470... Discriminator Loss: 1.4056... Generator Loss: 0.8884
Epoch 1/1, batch 2480... Discriminator Loss: 1.4206... Generator Loss: 0.7269
Epoch 1/1, batch 2490... Discriminator Loss: 1.3968... Generator Loss: 0.7432
Epoch 1/1, batch 2500... Discriminator Loss: 1.4456... Generator Loss: 0.7806
Epoch 1/1, batch 2510... Discriminator Loss: 1.4653... Generator Loss: 0.8009
Epoch 1/1, batch 2520... Discriminator Loss: 1.4472... Generator Loss: 0.7902
Epoch 1/1, batch 2530... Discriminator Loss: 1.4232... Generator Loss: 0.7587
Epoch 1/1, batch 2540... Discriminator Loss: 1.4118... Generator Loss: 0.7135
Epoch 1/1, batch 2550... Discriminator Loss: 1.4408... Generator Loss: 0.7969
Epoch 1/1, batch 2560... Discriminator Loss: 1.4423... Generator Loss: 0.7084
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Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.